Compositional Modeling of Impure CO2 Injection for Enhanced Oil Recovery and CO2 Storage
Bibliographic record
Abstract
Injecting CO2, a greenhouse gas, into the reservoir could be beneficial economically, by extracting remaining oil, and environmentally, by storing CO2 in the reservoir. CO2 captured from various sources always contains various impurities that affect the gas–oil system in the reservoir, changing oil productivity and CO2 geological storage performance. Therefore, it is necessary to examine the effect of impurities on both enhanced oil recovery (EOR) and carbon capture and storage (CCS) performance. For Canada Weyburn W3 fluid, a 2D compositional simulation of water-alternating-gas (WAG) injection was conducted to analyze the effect of impure CO2 on EOR and CCS performance. Most components in the CO2 stream such as CH4, H2, N2, O2, and Ar can unfavorably increase the MMP between the oil and gas mixture, while H2S decreased the MMP. MMP changed according to the type and concentration of impurity in the CO2 stream. Impurities in the CO2 stream also decreased both sweep efficiency and displacement efficiency, increased the IFT between gas and reservoir fluid, and hindered oil density reduction. The viscous gravity number increased by 59.6%, resulting in a decrease in vertical sweep efficiency. In the case of carbon storage, impurities decreased the performance of residual trapping by 4.1% and solubility trapping by 5.6% compared with pure CO2 WAG. As a result, impurities in CO2 reduced oil recovery by 9.2% and total CCS performance by 4.3%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".